Complexity of AI

Dear Colleagues,

Modern artificial intelligence (AI) models, specifically artificial neural networks, are typically complex systems that consist of a large number of interactions. With the accelerating progress made in the capabilities of AI, there is a growing need for a better understanding of their working principles. Complexity science, the field underlying Giorgio Parisi’s work which won the 2021 Nobel prize in physics and providing some of the key theoretical foundations for the AI contributions presented by John J. Hopfield and Geoffrey Hinton, which won the 2024 Nobel prize, is receiving increasing attention in relation to the pursuit of an understanding of AI models.

As complex systems, AI models typically exhibit the phenomena of phase transition, emergence (of intelligence), multiple meta-stabilities, chaos, self-organization, etc. Studying and analyzing AI models from the perspective of complexity science can pave the way for a deeper understanding of how AI models process information, carry out logic reasoning, and generate new data like images or text. Such findings can potentially help us to improve the capabilities, safety, and efficiency of AI models through untangling their complexities.

This Special Issue is organized in conjunction with the Focused Session entitled “Complexity of AI” at the Asia-Pacific Summer School and Conference on Networks and Complex Systems (APCNCS) 2026, held in Singapore (https://apcncs2026.github.io/). Authors are invited to contribute to both the Special Issue and the Focused Session. We welcome original research exploring the complexity of AI models from diverse perspectives, including methods, theories, applications, and empirical studies.

You may choose our Joint Special Issue in Complexities.

Dr. Siew Ann Cheong
Dr. Ling Feng
Dr. Lock Yue Chew
Guest Editors

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